metadata
license: apache-2.0
base_model: arun100/whisper-base-vi-1
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base Vietnamese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs vi_vn
type: google/fleurs
config: vi_vn
split: test
args: vi_vn
metrics:
- name: Wer
type: wer
value: 31.03382013835511
Whisper Base Vietnamese
This model is a fine-tuned version of arun100/whisper-base-vi-1 on the google/fleurs vi_vn dataset. It achieves the following results on the evaluation set:
- Loss: 0.6949
- Wer: 31.0338
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5823 | 43.0 | 500 | 0.7964 | 37.8978 |
| 0.3312 | 86.0 | 1000 | 0.6997 | 33.7125 |
| 0.2009 | 130.0 | 1500 | 0.6784 | 32.7479 |
| 0.1271 | 173.0 | 2000 | 0.6760 | 31.9985 |
| 0.0815 | 217.0 | 2500 | 0.6799 | 31.3028 |
| 0.0561 | 260.0 | 3000 | 0.6851 | 31.2337 |
| 0.0438 | 304.0 | 3500 | 0.6896 | 31.7256 |
| 0.0367 | 347.0 | 4000 | 0.6928 | 31.5949 |
| 0.0331 | 391.0 | 4500 | 0.6949 | 31.0338 |
| 0.0317 | 434.0 | 5000 | 0.6957 | 31.0453 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0